1. Association Mapping as a Breeding Strategy
Mark E. Sorrells and Flavio Breseghello
Department of Plant Breeding & Genetics
Cornell University

2. Presentation Overview
A Genetic Model for Association Mapping in Plant Breeding Populations
Comparison of Different Plant Breeding Materials for Association Mapping
Association Mapping of Kernel Size and Milling Quality in Soft Winter Wheat Cultivars

3. A Genetic Model for AM in Plant Breeding Populations: Association as Conditional Probabilities
Marker
Population genetics theory
(Hedrick 2005)
Recombination (c)
Breeding Pool
Gene={a}
Marker={m,M}
Selection on A or M (w)
Recombination (c)
New Parent (A,M)
t generations
Pr(A,M)=φ
Pr(a,M)=θ
Pr(a,m)=1-φ-θ
Pr(A,m)=0
Pr(A|M,c,t,φ,θ,w)
“Probability of a plant with marker allele M to have gene allele A, t generations after the introduction of A”

4. Recombination x initial frequency of M in the breeding pool
Freq. new parent: φ=0.05
Relative fitness: w=1
Freq. M in original population = θ
Freq. Recombination c ~8
~18
θ=0
θ=0.05
θ=0.25
θ=0
Pr(A|M)
A novel marker allele at 10 cM distance can be more predictive of the QTL allele than an allele 1 cM away if it was present in the original pop at a freq of 0.05
t Generations

5. Recombination x selection for M
Freq. new parent: φ=0.05
Relative fitness: w = 4 (red), 2 (green), 1.25 (blue)
Freq. M in original pop: 0
Freq. Recombination: c = 0.01, 0.05, 0.10
The generation at which the marker is depleted [Pr(A|M)=Pr(A)], depends on the selection intensity applied;
The final frequency of A depends on selection and tightness of linkage between marker and gene.
Pr(A|M)
Pr(A)
Generations

6. Summary
In plant breeding populations, the locus most associated with the trait is not necessarily the closest locus;
Loosely linked markers can still be useful for MAS if high intensity of selection is applied.

7. MAS for Complex Traits: Issues
Accurate detection and estimation of QTL effects
Pre-existing marker alleles in a breeding population can be linked to non-target QTL alleles
Multiple QTL alleles can have different relative values
Gene x gene and gene by environment interactions

8. Association Analysis as a Breeding Strategy
Most association studies have focused on estimating linkage disequilibrium and fine mapping.
Breeding programs are dynamic, complex genetic entities that require frequent evaluation of marker / phenotype relationships.
Breseghello, F., and M.E. Sorrells Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172:
Breseghello, F., and M.E. Sorrells Association analysis as a strategy for improvement of quantitative traits in plants. Crop Sci. In press.

9. Association Mapping versus QTL Mapping
Association Mapping can be conducted directly on the breeding material, therefore:
Direct inference from data analysis to breeding is possible
Phenotypic variation is observed for most traits of interest
Marker polymorphism is higher than in biparental populations
Routine variety trial evaluations provide phenotypic data
Association Mapping provides other useful information about:
Organization of genetic variation in relevant breeding populations
Novel alleles can be identified and their relative value can be assessed as often as necessary

10. Association Mapping versus QTL Mapping
Type I error (false positives) can be higher because of:
Unaccounted population structure
Simultaneous selection of combinations of alleles at different loci
High sampling variance of rare alleles
Type II error can be higher (low power) because of:
Lower LD than in biparental mapping populations
Unbalanced design due to differences in allele frequencies
A larger multiple-testing problem because of lower LD

11. Integration of Association Analysis in a Breeding Program
Germplasm
Parental
Selection
Hybridization
Elite germplasm
feeds back into
hybridization
nursery
New Populations
Marker Assisted Selection
Selection
(Intermating)
Novel & Validated
QTL/Marker
Associations
New Synthetics, Lines, Varieties
Evaluation Trials
Elite Synthetics, Lines, Varieties
Genotypic &
Phenotypic data

12. Types of Populations Germplasm Bank Collection Synthetic Populations
A collection of genetic resources including landraces, exotic material and wild relatives.
Synthetic Populations
Outcrossing populations (either male-sterile or manually crossed) synthesized from inbred lines. May be used for recurrent selection.
Elite Lines
Inbred lines (and checks) manipulated with the objective of releasing new varieties in the short term.

13. Characteristics Related to Association Mapping: Practical aspects
Aspects of AM
Germplasm bank
Synthetic Populations
Elite Germplasm
Sample
Core-collection
Segregating progenies
Elite lines and checks
Sample turnover
Static
Ephemeral
Gradually substituted
Source of phenotypic data
Screenings
Progeny tests
Yield trials
Type of traits
High heritability traits;
Domestication traits
Depends on the evaluation scheme
Low heritability traits: yield, resistance to abiotic stresses

14. Characteristics Related to Association Mapping: Genetic Expectations
Aspects of AM
Germplasm bank
Synthetic Populations
Elite Germplasm
Linkage Disequilibrium
Low
Intermediate and
fast-decaying
High
Population structure
Medium
Allele diversity among samples
Intermediate
Allele diversity within samples
Variable
1 or 2 alleles
(diploid species)
1 allele
(inbred lines)

15. Characteristics Related to Association Mapping: Potential Applications
Aspects
Germplasm bank
Synthetic Populations
Elite Germplasm
Power
Low
Intermediate and decreasing
High; could allow genome scan
Resolution
High; could allow fine mapping
Intermediate and increasing
Use of significant markers
Transfer of new alleles by marker-assisted backcross
Incorporation in selection index
Forward Breeding -MAS in progenies (requires validation)

16. Previous QTL information
Width 2D
Doubled-Haploid Population AC Reed x Grandin
QTL for kernel size (width) near Xwmc18-2D
Recombinant Inbred Population Synthetic W7984 x Opata (ITMI population)
QTL for kernel size (length) on 5A and 5B
Length 5B

17. Association Analysis Materials
95/149 soft winter wheat cultivars from the Northeastern US: Mostly recent releases, representing 35 seed companies / institutions
93 SSR loci: 33 on 2D, 20 on 5A, 9 on 5B, 31 on 16 other chromosomes
Rare alleles (freq<5%):considered as missing for LD and population structure analysis; considered as allele for AM analysis
Methods
Population Structure: 36 “unlinked” SSR markers- Structure without admixture, SPAGeDi (Hardy & Vekemans) program for Kinship ; Visualization: Factorial (Multiple) Correspondence Analysis (Benzecri, 1973 L' Analyse des correspondances. Dunod)
Linkage Disequilibrium: Tassel (maizegenetics.net) used to compute r2 , with p-values from 1000 permutations
Association Analysis: R stats package lme used to analyze Linear mixed-effects model with marker as fixed effects (selected from previously identified QTL regions) and subpopulations or Kinship as random effects (no obvious differentiating characteristics); Two-marker models: tested by likelihood ratio test
Jianming Yu, Gael Pressoir, et al. (2006) A Unified Mixed-Model Method for Association Mapping Accounting for Multiple Levels of Relatedness Nature Genetics 38:

18. Estimating Relatedness The K Matrixj .
Fij = (Qij-Qm)/(1-Qm)
(Ritland, Loiselle)
If Fij is negative, then it is set to zero.
F11 .
………….
Θij≅ Fij i
Fnj ……
Fnn
Relatedness (K)
In cattle studies the analogous matrix is estimated from pedigrees, and it controls for the polygene effect
Jianming Yu, Gael Pressoir, et al. (2006) A Unified Mixed-Model Method for Association Mapping Accounting for Multiple Levels of Relatedness Nature Genetics 38:

19. Population Structure: Sample Subdivisions
Subpopulation No. of Varieties Fst
Total
Moderate Population Subdivision

20. Population Structure: Factorial Correspondence Analysis
Orthogonal views of 4 soft winter wheat subpopulations S2 S3 S4 S1

21. Linkage Disequilibrium: Germplasm Sample Selection
R2 probability for unlinked SSR markers
149 lines genotyped with 18 unlinked SSR markers
Most similar lines were excluded
"Normalizing" the sample drastically reduced LD among unlinked markers
149 lines
95 lines

22. Definition of a baseline-LD specific for our sample
Defined as the 95th percentile of the distribution of r2 among unlinked loci
r2 estimates above this value are probably due to genetic linkage
Baseline LD for this sample: r2 =
Normal curve
Normal Distr. 95th percentile
LD baseline
LD baseline

23. Linkage Disequilibrium: Chromosome 2D
Consistent LD was below 1 cM, localized LD 1-5 cM

24. Linkage Disequilibrium: Chromosome 5A
Significant LD extended for 5 cM in pericentromeric region
~5 cM

25. Loci Associated with Kernel Size (p-values) Chromosome 2D
Agreed with
QTL in
Reed x Grandin
Kernel Size
Locus
Weight
Area
Length
Width cM
Name NY OH 7
Xcfd56
0.069
0.160
0.012
0.119
0.076
0.031
0.000*
0.252 11
Xwmc111
0.005
0.020
0.108
0.003’
0.107
0.000** 23
Xgwm261
0.145
0.016
0.019
0.009
0.027
0.058
0.001* 28
Xwmc112
0.057
0.047
0.120
0.480
0.367
0.024 64
Xgwm30
0.081
0.862
0.053
0.848
0.312
0.820
0.212 91
Xgwm539
0.042
0.038
0.030
0.039
0.290
0.334
Likelihood
Ratio Test **
Milling Quality
None of the loci on 2D were significant after multiple testing correction

26. Loci Associated with Kernel Size (p-values) Chromosome 5A
Agreed with
QTL in
M6 x Opata
n.s. **
Likelihood
Ratio Test
Kernel Size
Locus
Weight
Area
Length
Width cM
Name NY OH 55
Xcfa2250
0.021
0.007
0.044
0.014
0.002*
0.637
0.649
Xwmc150b
0.003
0.005
0.009
0.093
0.429 56
Xbarc117
0.118
0.022
0.039 60
Xbarc141
0.631
0.037
0.232
0.024
0.038
0.852
0.863
Milling Quality cM
Locus
Milling Score
Flour Yield
ESI
Friability
Break-Flour Yield 55
Xcfa2250
0.010
0.029
0.047
0.002*
0.081

27. B.L.U.E. of allele effects Kernel Length
N. of Cultivars:

28. B.L.U.E. of allele effects Kernel Width
N. of Cultivars:

29. B.L.U.E of allele effects
Kernel Weight
N. of Cultivars:

30. Conclusions Linkage Disequilibrium
Variation in LD across the genome can be characterized in relevant germplasm
Markers closely linked to QTL of interest can be identified and allelic effects quantified
Association Mapping as a Breeding Strategy
For recurrent selection, markers could be used to carry information from a “good year” to a “bad year”
In pedigree breeding, markers could carry information about traits of interest from replicated field trials to single row or single plant selection
Allelic values of previously identified alleles can be updated annually based on advanced trial data combined with genotypic data
New alleles can be identified and characterized to determine their relative value
A selection index can be used to incorporate both phenotypic and molecular data

31. Acknowledgements USDA Soft Wheat Quality Lab, Wooster, OH Embrapa
Technical Support:
David Benscher
James Tanaka
Gretchen Salm

32. Kangaroo Island
Wayne Powell